Question

我是Tensorflow＆amp; ML的新手并遵循以下示例： https://www.tensorflow.org/get_started/tflearn

在此处更改hidden_units参数之前，它的效果非常好：

classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                        hidden_units=[10, 20, 10],
                                        n_classes=3,
                                        model_dir="/tmp/iris_model")

当我尝试任何操作时，例如hidden_units = [20, 40, 20]或hidden_units = [20]会引发错误。

我试图自己找到但到目前为止没有成功，并认为这里有人可以提供帮助。问题是如何为DNN分类器选择一些隐藏层以及为什么上面两个我的例子不起作用？

这是完整的代码：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import urllib

import tensorflow as tf
import numpy as np

IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"

IRIS_TEST = "iris_test.csv"
IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"

if not os.path.exists(IRIS_TRAINING):
  raw = urllib.request.urlopen(IRIS_TRAINING_URL).read()
  with open(IRIS_TRAINING,'wb') as f:
    f.write(raw)

if not os.path.exists(IRIS_TEST):
  raw = urllib.request.urlopen(IRIS_TEST_URL).read()
  with open(IRIS_TEST,'wb') as f:
    f.write(raw)

# Load datasets.
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32)

# Specify that all features have real-value data
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10, 20, 10],
                                            n_classes=3,
                                            model_dir="/tmp/iris_model")
# Define the training inputs
def get_train_inputs():
  x = tf.constant(training_set.data)
  y = tf.constant(training_set.target)

  return x, y

# Fit model.
classifier.fit(input_fn=get_train_inputs, steps=2000)
# Define the test inputs
def get_test_inputs():
  x = tf.constant(test_set.data)
  y = tf.constant(test_set.target)

  return x, y

# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=get_test_inputs,
                                     steps=1)["accuracy"]

print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

Answer 1

发现它，如果未指定model_dir，则表示moel与新hidden_units

一起正常工作

如何在DNNClassifier中配置隐藏层

1 个答案: